Abstract
The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi–based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.
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Rajamani, G., Subramani, J. Geospatial multi-scale GNN for urban food security in climate-stressed environments.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-53396-5
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DOI: https://doi.org/10.1038/s41598-026-53396-5
Keywords
- Early warning systems
- Proximal remote sensing
- Geospatial monitoring
- Graph neural networks
- Improved sparrow search algorithm
- Food security
- Climate change adaptation
- Smart farming
- Nutrient management
Source: Ecology - nature.com
